Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It
is emerging as a standard tool for extracting knowledge from gene expression …

Background Biclustering of gene expression data searches for local patterns of gene
expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression …

Motivation: In recent years, there have been various efforts to overcome the limitations of
standard clustering approaches for the analysis of gene expression data by grouping genes …

The need to analyze high-dimension biological data is driving the development of new data
mining methods. Biclustering algorithms have been successfully applied to gene expression …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

Biclustering is a powerful data mining technique that allows clustering of rows and columns,
simultaneously, in a matrix-format data set. It was first applied to gene expression data in …

Background Biclustering has been largely applied for the unsupervised analysis of
biological data, being recognised today as a key technique to discover putative modules in …

DNA microarray technologies are used extensively to profile the expression levels of
thousands of genes under various conditions, yielding extremely large data-matrices. Thus …

Motivation The biclustering of large-scale gene expression data holds promising potential
for detecting condition-specific functional gene modules (ie biclusters). However, existing …

Background In a number of domains, like in DNA microarray data analysis, we need to
cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify …